ONLINE COURSE – Python for data science, machine learning, and scientific computing (PDMS02) This course will be delivered live
4 May 2020 - 8 May 2020
This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Python is one of the most widely used and highly valued programming languages in the world, and is especially widely used in data science, machine learning, and in other scientific computing applications. This course provides both a general introduction to programming with Python and a comprehensive introduction to using Python for data science, machine learning, and scientific computing. The major topics that we will cover include the following: the fundamentals of general purpose programming in Python; using Jupyter notebooks as a reproducible interactive Python programming environment; numerical computing using numpy; data processing and manipulations using pandas; data visualization using matplotlib, seaborn, ggplot, bokeh, altair, etc; symbolic mathematics using sympy; data science and machine learning using scikit-learn, keras, and tensorflow; Bayesian modelling using PyMC3 and PyStan; high performance computing with Cython, Numba, IPyParallel, Dask. Overall, this course aims to provide a solid introduction to Python generally as a programming language, and to its principal tools for doing data science, machine learning, and scientific computing. (Note that this course will focus on Python 3 exclusively given that Python 2 has now reached it end of life).
To find out more or to book online via our sister company (PS statistics) use the link below…
The instructors were excellent and clearly were the reasons for my previous comments. They both combined a deep understanding of statistics and ecology at the same level.Any questions or queries I’ve had, were thus first answered with an ecological point of view and then translated into statistical consideration thereby making much more sense on both side.In addition the course was very well organised, the course director and the two instructors were very friendly as well as professional. On the top of learning many useful things, I’ve also had a very good time during the week there.” Clement Garcia,
Spatial ecologist, Centre For Environment, Fisheries & Aquaculture Science (CEFAS), England
(Attended ADVR course)